The field of artificial intelligence (AI) is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data and use the insights learned to improve decision making.

What AI can’t do, at least on its own, is explain the thought processes behind their decisions. Those in data science are now turning their efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand.

Reinforcement learning is a field of AI and it studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions. This approach has led to algorithms that have independently learned to play chess at a superhuman level and compete on television game shows.

When AI is able to explain decisions in a human-readable format, the value it provides to organizations reaches beyond just knowing how the decision was made, especially if the decision is of a complex nature that augments an expert analyst’s judgement.

In some industries, it will not always be necessary for a machine to fully explain whether it did what it was expected to do, but in many, i.e. healthcare and finance, for example, the need is critical. The value goes far beyond just being able to prove to an auditor that the right decision was taken. Explainable AI is about getting people to trust and buy into these new systems and how they’re changing the way we work.

A company’s semantic model is an ever evolving reflection of their business assets and relationships to the industry and the world. Creating a taxonomy and thesaurus is the foundation, but expanding that into a robust semantic model takes intelligence. Data Harmony’s semantic tools allow for concept identification and recommendations.

Finding concepts takes AI to delve through your document collection to identify and classify concepts and allows you to expand your semantic model to create meaning and relationships. The Data Harmony suite expands the semantic model to build and deliver the ontology you need for semantic search. A better semantic search will increase the likelihood of finding what you need, when you need it, and creating the transaction or outcome you desire.

Melody K. Smith

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.